{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dux/NeuralForceField/models\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from matplotlib.lines import Line2D\n",
    "from nff.io.ase_calcs import EnsembleNFF, NeuralFF\n",
    "from nff.utils.cuda import cuda_devices_sorted_by_free_mem\n",
    "from scipy.cluster.hierarchy import dendrogram, fcluster, linkage\n",
    "from sklearn.decomposition import PCA\n",
    "from tqdm import tqdm\n",
    "\n",
    "from mcmc.calculators import get_embeddings_single, get_results_single, get_std_devs_single\n",
    "from mcmc.utils.misc import get_atoms_batch\n",
    "from mcmc.utils.plot import DEFAULT_DPI"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load neural network force field. We are using neural network weights from our Zenodo dataset (https://zenodo.org/record/7927039)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEVICE = f\"cuda:{cuda_devices_sorted_by_free_mem()[-1]}\" if torch.cuda.is_available() else \"cpu\"\n",
    "NFF_CUTOFF = 5.0\n",
    "\n",
    "# requires an ensemble of models in this path\n",
    "nnids = [\"model01\", \"model02\", \"model03\"]\n",
    "model_dirs = [\n",
    "    os.path.join(\n",
    "        os.getcwd(),\n",
    "        \"data/SrTiO3_001/nff\",\n",
    "        str(x),\n",
    "        \"best_model\",\n",
    "    )\n",
    "    for x in nnids\n",
    "]\n",
    "\n",
    "models = []\n",
    "for modeldir in model_dirs:\n",
    "    m = NeuralFF.from_file(modeldir, device=DEVICE).model\n",
    "    models.append(m)\n",
    "\n",
    "# EnsembleNFF for force standard deviation prediction\n",
    "ensemble_calc = EnsembleNFF(\n",
    "    models,\n",
    "    device=DEVICE,\n",
    "    model_units=\"kcal/mol\",\n",
    "    prediction_units=\"eV\",\n",
    ")\n",
    "# NeuralFF for latent space embedding calculation\n",
    "single_calc = NeuralFF(\n",
    "    models[0],\n",
    "    device=DEVICE,\n",
    "    model_units=\"kcal/mol\",\n",
    "    prediction_units=\"eV\",\n",
    "    properties=[\"energy\", \"forces\", \"embedding\"],\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load surfaces previously obtained from VSSR-MC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final dataset has 100 entries\n"
     ]
    }
   ],
   "source": [
    "with open(\"data/SrTiO3_001/SrTiO3_001_2x2_mcmc_structures_100.pkl\", \"rb\") as f:\n",
    "    dataset = pickle.load(f)\n",
    "\n",
    "print(f\"Final dataset has {len(dataset)} entries\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract embeddings and force standard deviation as the metric value for clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:14<00:00,  7.06it/s]\n"
     ]
    }
   ],
   "source": [
    "embeddings = []\n",
    "metric_values = []\n",
    "for single_dset in tqdm(dataset):\n",
    "    atoms_batch = get_atoms_batch(single_dset, NFF_CUTOFF, device=DEVICE)\n",
    "    single_calc_results = get_results_single(atoms_batch, single_calc)\n",
    "    embedding = get_embeddings_single(\n",
    "        atoms_batch,\n",
    "        single_calc,\n",
    "        results_cache=single_calc_results,\n",
    "        flatten=True,\n",
    "        flatten_axis=0,\n",
    "    )\n",
    "    metric_value = get_std_devs_single(atoms_batch, ensemble_calc)\n",
    "    embeddings.append(embedding)\n",
    "    metric_values.append(metric_value)\n",
    "embeddings = np.stack(embeddings)\n",
    "metric_values = np.stack(metric_values)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtain PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pca explained ratios are [0.70155203 0.25356615 0.01665298 0.00797125 0.00539697]...\n"
     ]
    }
   ],
   "source": [
    "X_embeddings = np.stack(embeddings)\n",
    "\n",
    "pca = PCA(n_components=32, whiten=True).fit(X_embeddings)\n",
    "\n",
    "print(f\"The pca explained ratios are {pca.explained_variance_ratio_[:5]}...\")\n",
    "\n",
    "# transform train\n",
    "X_r = pca.transform(X_embeddings)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 14 clusters\n"
     ]
    }
   ],
   "source": [
    "Z = linkage(X_r[:, :3], method=\"ward\", metric=\"euclidean\", optimal_ordering=True)\n",
    "y = fcluster(Z, t=2, criterion=\"distance\", depth=2)  # t sets the distance\n",
    "clusters = np.unique(y)\n",
    "max_index = np.max(clusters)\n",
    "\n",
    "print(f\"There are {len(clusters)} clusters\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize with dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkVXJhQ0NDQ+y6665RVlYWZWVlcdJJJyW/xiuvvBIXXXRRHHjggTFy5Mjo27dv9OzZM9Zff/3Ye++945xzzompU6cmvy4AAAB0BRXFbuCjrrvuupg2bVq7jP3GG2/EN77xjfjzn//c7PF58+bFvHnz4sknn4wrr7wy9tprr/j5z38eu+yyS7v0AwAAAFlUUjMbnnvuuTjnnHPaZey77rordtxxx9UGDc158sknY9SoUfHTn/60XXoCAACALCqZsOHNN9+MsWPHRm1tbfKx//jHP8YxxxwTS5cubfW59fX1ceaZZ8Zll12WvC8AAADIopIIG2bNmhWf+tSnorKyMvnY//rXv+Kkk06K+vr6JvWePXvGySefHA888EDMmDEjZsyYEffee28cf/zx0aNHj7xxzj///HjggQeS9wcAAABZU/Q9G1544YUYO3ZszJkzJ/nYDQ0NceKJJ0ZNTU2T+lZbbRV33HFH7LDDDk3qW2+9dYwdOzbOPvvsOOaYY+Lf//73h8caGxvjlFNOiVdffTX69euXvFcAAADIiqLObLjjjjti7733bpegISLi5ptvjueff75JbdiwYfHggw/mBQ2r+sQnPhGPPvpojBw5skn9nXfeiSuvvLJdegUAAICsKErYsGTJkjjllFPi6KOPLmgfhZa6/PLL82q/+MUvYuONN17ruRtuuGHcfffd0a1b06fo6quvbteeAQAAoLPr8LDhjjvuiG233TZ+/etf5x2rqEi3quPRRx+NGTNmNKnttNNOceSRR7Z4jF122SWOO+64JrUFCxbEnXfemaJFAAAAyKQOCxv+8Y9/xOjRo+Poo49udtnE1ltvHddff32y6/3xj3/Mq5188smtHucrX/lKXu3WW28tqCcAAADoCjosbNh///3jiSeeaPbYuHHj4tlnn43NNtss2fUefPDBvNrhhx/e6nH22muvGDx4cJPaI488krfpJAAAAPCBom4QOXTo0Ljtttvitttui/79+ycbt7KyMl577bUmteHDh8cmm2zS6rHKy8tj1KhRTWq1tbXx7LPPtqVFAAAAyKyihA09e/aMs88+O2bMmBHjxo1LPv5LL72UV/vEJz5R8HjNnfvPf/6z4PEAAAAgy9LtyNgC3bt3j/Hjx8fFF1/cojtCFGr69Ol5tW222abg8bbccsu82quvvlrweAAAAJBlHRY2nH/++XHqqafGhhtu2O7XmjVrVl6tLeHGiBEjWnQNAAAAoAPDhokTJ3bUpeLdd9/Nq7Ul5Bg2bFherbKysuDxAAAAIMs6dBlFR2kubBg0aFDB4w0cODCvtnDhwlaP09wtP1clwAAAACALMhk2VFVV5dUGDBhQ8HjN3Slj8eLFrR5n5MiRBfcAAAAAnUVRb33ZXurq6vJqvXr1Kni8nj17tugaAAAAQEZnNjQXBFRUFP6pNnfuihUrWj3O7Nmz13i8srIydt9991aPCwAAAKUkk2FDfX19Xq28vLzg8VZ3bmNjY5SVlbV4nObuagEAAABZk8llFN27d8+r5XK5gsdrbhZD9+7dWxU0AAAAQFeRybChR48eebVClj2s1FxQ0dw1AAAAgIyGDf369curLV26tODxlixZkldry4aTAAAAkGWZDBuGDBmSV6uuri54vObOXW+99QoeDwAAALIsk2FDc0HAe++9V/B4zZ07dOjQgscDAACALMtk2LDJJpvk1ebOnVvweM2du8EGGxQ8HgAAAGRZJsOGzTffPK/2+uuvFzxec+dus802BY8HAAAAWZbJsGHHHXfMq73yyisFj9fcudtvv33B4wEAAECWZTJs2H777aNPnz5NatOmTYuGhoaCxps6dWpebddddy1oLAAAAMi6TIYN5eXlsc8++zSpVVdXxzPPPNPqsd555514+eWXm9S22GKLZveFAAAAADIaNkREjBkzJq82adKkVo8zadKkaGxsbFI75JBDCu4LAAAAsi6zYcO4ceOioqKiSe23v/1tvP/++y0eo7a2Nn7+85/n1U8++eQ29wcAAABZldmwYejQoXHkkUc2qS1evDhOOeWUFu/dMGHChJg1a1aT2p577hkf//jH0zQJAAAAGZTZsCEi4sILL4xu3Zp+in/605/i9NNPX2vg8MMf/jCuvfbavPrll1+etEcAAADImkyHDTvssEOcdtppefVrr702DjjggHjhhRfyjr3++utxzDHHxHe/+928Y8cff3zsu+++7dIrAAAAZEXF2h/Suf3whz+Mxx9/PJ5//vkm9UceeSQ+/vGPx4477hhbb711dO/ePV5//fV45pln8jaEjIjYeuut45e//GVHtQ0AAACdVubDht69e8cDDzwQBx10UPzrX//KO/7iiy/Giy++uMYxttpqq3jooYeiX79+7dUmAAAAZEaml1GsNGzYsHj88cdj/PjxrT73yCOPjMcffzxGjBjRDp0BAABA9nSJsCEiYp111ok//OEPMWXKlDj88MOje/fuq31st27d4tOf/nTcf//9cffdd8d6663XgZ0CAABA51ZSyyj222+/ZvdLSGmfffaJffbZJ5YsWRLPPPNMzJw5MxYsWBDdunWLQYMGxaabbhp77LFHDBgwoF37AAAAgKwqqbChI/Xr1y/233//2H///YvdCgAAAGRKl1lGAQAAAHQMYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACCpimI3UAz/7//9v3jggQfi0Ucfjddffz3ef//9qKmpicGDB8e6664bO++8c3zyk5+MI488MoYMGVLsdgEAAKBT6VJhw7PPPhvnn39+/O1vf2v2+LvvvhvvvvtuTJ8+PW6++eY4/fTT48tf/nJ8//vfj0GDBnVwtwAAANA5dZllFD/96U9j7733Xm3Q0Jyampr4+c9/Hh//+Mfj6aefbsfuAAAAIDu6RNjwgx/8IM4888xYsWJFQee/9dZbsf/++8eTTz6ZuDMAAADInsyHDbfffnt8//vfz6t369YtjjrqqLj11ltj+vTp8dZbb8W0adPimmuuiZ122inv8cuWLYvDDjss3nzzzQ7oGgAAADqvTIcNS5cujTPPPDOvPnz48HjsscfijjvuiGOPPTa22267GDlyZOy8885x2mmnxfPPPx9XXXVVlJeXNzlvwYIFcfbZZ3dU+wAAANApZTps+M1vfhPvvPNOk9rAgQPj4Ycfjr322mu155WVlcVZZ50VV199dd6xO++8M/71r38l7xUAAACyItNhw913351Xu+iii2Lrrbdu0flf//rXY5999smr33nnnW3uDQAAALIq02HDRzd07NatW5x44omtGuPkk0/Oq7kzBQAAAKxeZsOG+fPnx/Lly5vU1l133Rg8eHCrxtlhhx3yah9dmgEAAAD8n8yGDUuXLs2rFXLry+7du+fVPhpiAAAAAP8ns2FDczMYFi5cGG+//XarxpkxY0ZebcMNNyy4LwAAAMi6zIYN/fr1i8033zyvfsstt7RqnJtvvjmvtqY7WQAAAEBXl9mwISLiiCOOyKtddtllMXPmzBadf8cdd8R9993XpFZeXt7qTSYBAACgK8l02HDGGWdEnz59mtQWL14cBx10UPzzn/9c47m33HJLnHDCCXn1b3zjG7Hlllsm7RMAAACyJNNhw0YbbRQ///nP8+pvvfVW7LHHHnHsscfGH//4x3jllVdizpw58eKLL8ZvfvOb2HfffeOEE06I2traJud9+tOfjiuuuKKj2gcAAIBOqaLYDbS3//qv/4q6uro444wzIpfLfVivr6+PSZMmxaRJk9Y6Rnl5eXzrW9+Kyy67rNm7UwAAAAD/J/NhQ0TEqaeeGnvvvXecddZZ8fe//71V544bNy4uvPDC2G677drcx5w5c9Z4vLKyss3XAAAAgGLrEmFDRMROO+0Ul1xySUREqwKHv/71r7HhhhvGeeedF+uuu26behg5cmSbzgcAAIDOINN7Nqz0xBNPxJ577hl77713q2c2LFq0KH7yk5/EVlttFbfeems7dQgAAADZkfmZDVdeeWV897vfjfr6+ib1jTfeOL72ta/FwQcfHJtsskn06dMn5s2bF9OmTYs//elPccsttzTZ42HhwoVx3HHHxaxZs+Lcc88tqJfZs2ev8XhlZWXsvvvuBY0NAAAApSLTYcNPf/rTOOecc/Lq5557blx00UXRs2fPJvURI0bEiBEj4ogjjojzzz8/xo8fn3eLzPPOOy822WSTGD9+fKv7GTFiRKvPAQAAgM4ms8soXnjhhWaDhuuuuy4uvfTSvKDho7bYYouYMmVKHHDAAXnHTjvttHj//feT9QoAAABZktmwYcKECU2WQUREnHzyyfH1r3+9xWP07t07/vjHP8ZGG23UpL5w4cK44oorkvQJAAAAWZPJsOH111+Phx9+uEmtT58+cemll7Z6rEGDBn14F4tV3XTTTXn7QAAAAAAZDRsefPDBaGxsbFIbM2ZMDB06tKDxxo8fHwMGDGhSe/fdd2P69OkF9wgAAABZlcmwYdq0aXm1/fbbr+DxunfvHvvuu29e/fnnny94TAAAAMiqTIYN7733Xl5tww03bNOYI0eOzKvZJBIAAADyZTJsqK2tzat169a2T7Vfv355tRUrVrRpTAAAAMiiTIYN6667bl7trbfeatOYzc1iKHQPCAAAAMiyTIYNw4YNy6s999xzbRqzuf0ZNthggzaNCQAAAFmUybBhzz33zKvdeeedsWzZsoLGe+utt+KFF15oUuvevXuz1wEAAICuLpNhw8EHHxzdu3dvUquuro5f/vKXBY03ceLEvFtpjh49OtZZZ52CewQAAICsymTYMGDAgBg3blxe/bzzzovp06e3aqzJkyfHr3/967z6aaedVnB/AAAAkGWZDBsiIi655JLo2bNnk1ptbW0cdNBB8dhjj7VojD/96U9x3HHH5c1q2GOPPeKoo45K1isAAABkSWbDhk022SQuvfTSvHplZWXsv//+MWHChJg9e3az586cOTO++MUvxuc+97lYunRpk2MDBgxodqYDAAAA8IGKYjfQns4666yYOXNmXH/99U3quVwurrrqqvjxj38cO+ywQ2y33XbRt2/fWLBgQUyfPj1mzpzZ7Hjl5eUxadKk2H777TuifQAAAOiUMh02RERce+21MXDgwPjRj36UtxyisbExXnzxxXjxxRfXOs7gwYPj1ltvjU9/+tPt1SoAAABkQmaXUaxUXl4el19+efzpT3+KTTfdtKAxDjrooPjnP/8paAAAAIAWyHzYsNLhhx8eM2fOjJtuuin222+/vM0jP2rIkCExbty4eOqpp+LBBx8sOKgAAACAribzyyhWVVFRESeeeGKceOKJsWzZspg6dWrMmTMn5s+fH0uXLo2BAwfGuuuuG1tuuWXstNNO0a1bl8liAAAAIJkuFTasqnfv3rHffvsVuw0AAADIHG/dAwAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkqoodgMALVafi6hdVOwuKDU1K5qpLYgo697xvVD6eg2MKPfrDwC0Nz9tgc7hhUkRD5wTUbe42J1Qahr7R8T1TWvX7hZRVl2UdihxPdeJGHNFxE7jit0JAGRalw0bZsyYEXfeeWc89thj8eqrr8a8efOirq4uBg0aFOuvv37stttu8alPfSo+//nPR58+fYrdLnRt9TlBA5BG3eIPvp987CgzHACgHXW5n7LTpk2L7373u/HQQw81e3zevHkxb968mD59etx4441x1llnxdlnnx3f/va3o7y8vIO7BSLig6UTggZWY0hZdczqdVyx26AzqVv8wfeVvusWuxMAyKwus0FkY2Nj/OAHP4hRo0atNmhozvz58+O8886LffbZJ+bOnduOHQIAAEA2dImZDQ0NDXHSSSfFzTffXPAYTz31VBx44IHx6KOPxpAhQxJ2BxTktGci+vi/CLRAzfyIa3cvdhcA0KV0ibDhrLPOajZoGDBgQHzpS1+KMWPGxOabbx5lZWXx1ltvxV/+8pe4/vrrY9GiRU0eP3369PjiF78Y9913Xwd1DqxWnyGmQAMAQInKfNhw7733xs9+9rO8+kEHHRS/+93vYtiwYU3qm222Wey3335xzjnnxBe+8IX4y1/+0uT4/fffH3feeWccddRR7do3AAAAdFaZ3rOhtrY2TjvttLz6scceG3/+85/zgoZVDRkyJO6777444IAD8o5dddVVSfsEAACALMl02PCLX/wiZs+e3aS22267xU033dSiO0uUl5fHL3/5y6ioaDoB5KmnnopXX301aa8AAACQFZkNGxobG+Oaa65pUquoqIgbb7wxevTo0eJxtthiizjkkEPy6o8++mibewQAAIAsymzY8Mgjj8Qbb7zRpHbCCSfEdttt1+qxPvvZz3749169esXw4cPjvffea3OPAAAAkEWZ3SDynnvuyaudfvrpBY117LHHxv777x/rrrtu9O3bt62tAQAAQKZlNmz46F0ktthii/j4xz9e0Fi9e/eOjTfeOEFXAAAAkH2ZXEaxaNGimDlzZpPafvvtV5xmAAAAoIvJZNgwbdq0aGxsbFLbZZdd1njO8uXLY/78+ZHL5dqzNQAAAMi8TIYNzd2Wcsstt2zy8YoVK+L222+P8ePHx8Ybbxw9e/aMddddN3r06BFDhw6NMWPGxK9+9atYuHBhR7UNAAAAmZDJsOH111/Pq2244YYf/v13v/tdbLrppjFu3Li47bbb4q233vrwWGNjY7z33nvx5z//Ob761a/GFltsET//+c/NeAAAAIAWymTY8Pbbb+fV1l9//Vi6dGl8/vOfjy9+8YvNPqY5CxYsiDPOOCMOOeSQqKqqSt0qAAAAZE4m70bx/vvv59UaGxvj0EMPjUcffbSgMR966KEYPXp0PProozFo0KCCxpgzZ84aj1dWVhY0LgAAAJSSTIYNze2zcOaZZ+YFDZ/73OfiuOOOi1GjRsV6660XVVVV8eqrr8bdd98d119/fSxdurTJ41966aX4whe+EPfdd1+UlZW1uq+RI0e2+hwAAADobDK5jGLZsmV5tVtuueXDvw8fPjyeeOKJuPPOO+Ooo46KESNGRM+ePWO99daL0aNHx1VXXRWvvvpq7L333nnjPPDAA/GTn/ykXfsHAACAziyTMxuWL1++2mMbbbRRPPnkkzF8+PA1jjF8+PD429/+Fp/5zGdiypQpTY5ddtll8dWvfjX69u3bqr5mz569xuOVlZWx++67t2pMAAAAKDWZDBsaGhqarZeXl8cdd9yx1qBhpd69e8cf/vCH+NjHPhaLFi36sP7+++/H//zP/8S3vvWtVvU1YsSIVj0eAAAAOqNMLqPo3r17s/Xx48fHbrvt1qqxhg8fHqeeempe/Z577imoNwAAAMi6TIYNPXv2bLb+ta99raDxTjnllLzaU0891ezeEAAAANDVZTJsGDhwYF6tX79+MWrUqILG22STTfKWQNTV1cXLL79c0HgAAACQZZkMG9Zdd9282iabbBIVFYVvUbHNNtvk1ebNm1fweAAAAJBVmQwbmtuIcfDgwW0as7nz33///TaNCQAAAFmUybBhiy22yKutWLEi+XW6dcvk0wcAAABtkslXy9tvv31e7e23327TmAsWLMirrbPOOm0aEwAAALIok2HDbrvtljfrYPbs2W1a9vD666/n1ZqbQQEAAABdXSbDhgEDBsQuu+zSpNbY2Bh//vOfCxqvsrIy/vOf/zSp9evXT9gAAAAAzchk2BARcfjhh+fVfv3rXxc01u9///u82oEHHhjl5eUFjQcAAABZltmw4cQTT8wLA6ZMmRIPPvhgq8apqamJq6++Oq8+bty4NvUHAAAAWZXZsGGjjTaKo446Kq/+5S9/uVV7N5x55pnx1ltvNakNHz682bEBAACADIcNERGXXnpp9OzZs0lt9uzZceCBB8acOXPWev4FF1wQv/rVr/Lql1xySXTv3j1ZnwAAAJAlmQ4bNt9887jiiivy6i+88ELstNNOcfXVV8eSJUvyjr/88stxyCGHxMSJE/OOffKTn4yTTjqpPdoFAACATKgodgPt7Zvf/GbMmDEjrrvuuib1BQsWxOmnnx7nnntu7LHHHrHRRhvF0qVL45VXXomXXnqp2bFGjBgRt912W5SVlXVE6wAAAJnTUN8QdTW5YreRp3bpivzakhWxrLH0Xv/17FMR3cpLe+5A5sOGiIhrrrkm+vfvHz/84Q/zji1dujQefvjhtY6x6aabxkMPPRTDhg1rjxYBAAAy79Wpc2PKbTNj+bLSCxtqyhoj1mla+8MPpkafEgwbevSuiH2P3Sq2HlW6r09LOwpJpKysLC6//PKYPHlyjBgxotXnjx8/Pp577rnYbLPN2qE7AACA7GuobyjZoKGzWb4sF1NumxkN9Q3FbmW1usTMhpUOO+ywOOigg+KGG26I3/zmN/Hcc8+t9rG9e/eOww47LM4+++zYfffdO7BLAACA7KmryZV00NCnsSzOWdS72G202PJluairyUXv/j2K3UqzulTYEBHRq1evOPXUU+PUU0+Nd999N/75z3/GG2+8EVVVVVFeXh6DBw+O7bbbLnbeeefo06dPsdsFAACATqfLhQ2rGjp0aBx66KHFbgMAAKBLGn/RqOjdr3ux2+gUli1ZEbf+YGqx22ixLh02AAAAUDy9+3Uv2WUAtE2X2CASAAAA6DjCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkFRFsRsAAADyNeZyUV9VVew2CpKrWZFfW7Qocsu7F6GbtisfMCDKKrx0gtbwPwYAAErM4smTY+4lE6OhurrYrRRkUY++EWN+0KT2xphDY8HypUXqqG269e8fwy74Xqxz+OHFbgU6DcsoAACghDTmcp06aMiihurqmHvJxGjM5YrdCnQaZjYAAEAJqa+q6vRBw8DlS+PPf5pQ7DaSaqiujvqqqqgYPLjYrUCnYGYDAAAAkJSZDQAAUOI2u/++KB80qNhtdCn1CxfGG4eOLXYb0GkJGwAAoMSVDxpk+j7QqVhGAQAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWHDavzoRz+KsrKyD/9ssskmxW4JAAAAOgVhQzNefPHFuOCCC4rdBgAAAHRKwoaPqKuri+OPPz6WL19e7FYAAACgUxI2fMR5550X//rXv4rdBgAAAHRawoZV/OMf/4if/OQnxW4DAAAAOjVhw/9atGhRfPGLX4zGxsZitwIAAACdmrDhf5122mkxe/bsYrcBAAAAnZ6wISImTZoUf/jDH4rdBgAAAGRClw8b3n777fj617/epPa1r32tSN0AAABA59elw4bGxsY46aSTYuHChR/Wtthii7jyyiuL2BUAAAB0bl06bPjZz34WDz300Icfl5eXx0033RR9+/YtYlcAAADQuXXZsOHll1+Oc889t0nt29/+duy1115F6ggAAACyoUuGDStWrIgvfOELUVtb+2Ftp512iu9///vFawoAAAAyokuGDRdccEE8//zzH37cs2fPuPnmm6NHjx5F7AoAAACyocuFDY8//nhcccUVTWoXX3xx7LDDDkXqCAAAALKlS4UN1dXVccIJJ0RDQ8OHtdGjR8eECROK2BUAAABkS5cKG04//fSYNWvWhx/369cvbrrppujWrUs9DQAAANCuKordQEe566674sYbb2xS+/GPfxybbbZZh/UwZ86cNR6vrKzsoE4AAACg/XSJsGHu3Lnx1a9+tUnt0EMPjVNOOaVD+xg5cmSHXg8AAACKoUusHzj55JPj/fff//DjIUOGxK9//esidgQAAADZlfmZDdddd138+c9/blL7xS9+EcOGDevwXmbPnr3G45WVlbH77rt3UDcAAADQPjIdNsycOTPOOeecJrXjjz8+jj766KL0M2LEiKJcFwAAADpSZpdR5HK5+MIXvhA1NTUf1kaMGBHXXHNNEbsCAACA7Mts2HDxxRfHs88+++HHZWVlccMNN8TAgQOL1xQAAAB0AZkMG55++um49NJLm9ROPfXUOOigg4rUEQAAAHQdmQsbli5dGieccELU19d/WNtqq63iRz/6URG7AgAAgK4jc2HDs88+G//+97+b1GbOnBl9+/aNsrKyFv/5qDfffLPZxz3yyCMd9JkBAABA55C5sAEAAAAoLmEDAAAAkJSwAQAAAEiqotgNpLbXXntFZWVlm8fZYIMNmnw8YsSIJrfSXGnw4MFtvhYAAABkSebChh49esSwYcOSj1teXt4u4wIAAEDWWEYBAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASKqi2A2UqsbGxmK3AAAAAJ2SmQ0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkFRFsRsAVqM+F1G7qNhdlIaa+S2rdWW9BkaU+5YOAEBp8JsplKIXJkU8cE5E3eJid1K6rt292B2Ulp7rRIy5ImKnccXuBAAALKOAklOfEzTQenWLP/h3U58rdicAACBsgJJTu0jQQGHqFlt6AwBASRA2AAAAAEnZswE6g9OeiegzpNhdUGpq5tu7AgCAkiRsgM6gz5CIvusWuwsAAIAWsYwCAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASKqi2A0AAFB6cg25qFpe1aS2qG5F3uMW1S2KsoruTWoDegyIim5+zaQ4GnO5qK+qWvsD1yK3cGGLaq1VPmBAlFX4/0H2+VcOAEAT975+b1w29bKoXlHdpN6Q6xsRFzSpHf6nI6JbxdImtf7d+8e5o86NwzY/rL1bhSYWT54ccy+ZGA3V1Wt/cAH+c+jYNo/RrX//GHbB92Kdww9P0BGULssoAAD4UK4h12zQ0BrVK6rjsqmXRa4hl7AzWLPGXK5dg4ZUGqqrY+4lE6Mx5/8H2WZmAwAAH6paXrXaoKFbxdLov+13WzRO9YrqqFpeFYN7DU7ZHqxWfVVVyQcNKzVUV0d9VVVUDPb/g+wyswEAAABIyswGAADW6J4j7omBvQau8TGLahfFEfcc0TENQQttdv99UT5oULHbiPqFC+ONBPs9QGcibAAAYI0G9hpoOQSdUvmgQZYqQJFYRgEAAAAkJWwAAAAAkhI2AAAAAEnZswEAAIC1aqhviLqaXMHnL1uyokW1lurZpyK6lXv/vFQJGwAAAFijV6fOjSm3zYzlywoPG5pz6w+mFnxuj94Vse+xW8XWo4Yl7IhUxEAAAACsVkN9Q7sEDW21fFkuptw2MxrqG4rdCs0QNgAAALBadTW5kgsaVlq+LNempR20H2EDAAAAkJQ9GwCAdOpzEbWLit1FUzXzW1Yrtl4DI8r9agZ0DuMvGhW9+3Xv8OsuW7KiTfs80HH8RAMA0nhhUsQD50TULS52J2t37e7F7iBfz3UixlwRsdO4YncCsFa9+3WP3v17FLsNSphlFABA29XnOk/QUKrqFn/wHNZbewxA5ydsAADarnaRoCGFusWltwwFAAogbAAAAACSsmcDANA+Tnsmos+QYndR2mrml+b+EQDQRsIGAKB99BkS0XfdYncBABSBsAEAANagMZeL+qqqDrtebuHCFtXaU/mAAVFW4aUCUDjfQQAAYDUWT54ccy+ZGA3V1UXt4z+Hju3Q63Xr3z+GXfC9WOfwwzv0ukB2dMmwoaGhIR566KGYMmVKPPnkk/Hmm2/GwoULo7q6OtZZZ50YMmRIbLrpprHvvvvGAQccEKNGjSp2ywAAdLDGXK4kgoZiaKiujrmXTIwBY8aY4QAUpEt958jlcnHdddfFT3/60/jPf/7T7GPmz58f8+fPj5kzZ8Zf//rXOP/882O33XaL888/P4444ogO7hgAgGKpr6rqkkHDSg3V1VFfVRUVgwcXuxWgE+oyt77897//HaNHj44zzjhjtUHD6jz77LNx5JFHxkknnRQ1NTXt1CEAAABkQ5eY2fDaa6/F3nvvHfPmzWvTODfddFPMmDEj/v73v0efPn0SdQcAQGex2f33RfmgQcVuo13UL1wYb3Tw3hBAdmU+bJg/f34cdNBBzQYN6623Xpx00kmx//77x0YbbRT9+/ePBQsWxIsvvhiTJ0+Ou+66KxoaGpqcM3Xq1Bg/fnz86U9/irKyso76NAAAKAHlgwZZVgDQApkPG773ve/Fm2++mVc//fTT49JLL42+ffs2qY8cOTJ22mmnOOGEE2L69Okxbty4mD59epPHTJ48OW6++eY48cQT27V3AAAA6IwyvWfDjBkz4n/+53/y6pdddln87Gc/ywsaPmr77bePp556Knbccce8Y+edd17U1dUl6xUAAACyItNhw0033RT19fVNagcffHB897vfbfEY/fv3jzvvvDN69OjRpP7222/H3//+9yR9AgAAQJZkOmy4/fbb82oXXnhhq8fZYost4gtf+EJe/d577y2oLwAAAMiyzIYNlZWV8cYbbzSprb/++rHXXnsVNN7Ysfk7886cObOgsQAAACDLMhs2vPzyy3m1XXfdteDxNt1007za3LlzCx4PAAAAsiqzd6MYNGhQnHrqqfHOO+/EO++8E2+//XZssMEGBY/30VtgRkSUl5e3pUUAAADIpMyGDTvvvHPsvPPOycb76JKMiIjhw4cnGx8AAACyIrPLKFL7y1/+klfbeuuti9AJAAAAlDZhQwssWLAgJk2alFc/4ogjitANAAAAlLbMLqNI6Yc//GEsWbKkSW3o0KGxzz77FKkjAGiF+lxE7aL2vUbN/JbVUus1MKLcrzMAUGr8dF6L5557Ln784x/n1c8880wbRAJQ+l6YFPHAORF1izv+2tfu3v7X6LlOxJgrInYa1/7XAgBaTNiwBosWLYpjjz02crlck/rmm28eZ5xxRqvHmzNnzhqPV1ZWtnpMAFit+lzxgoaOUrf4g8/xY0eZ4QAAJcRP5dWoq6uLz33uc/Haa681qZeXl8eNN94YvXr1avWYI0eOTNUeAKxd7aJsBw0r1S3+4HPtu26xOwEA/pcNIpuxYsWKGDduXPzjH//IO3b55ZfH6NGji9AVAAAAdA5mNnxEXV1dHHPMMTF58uS8Y1/5yldiwoQJBY89e/bsNR6vrKyM3XfvgPWtAHRdpz0T0WdIsbtom5r5HbMfBABQMGHDKpYsWRKf/exn46GHHso7Nn78+PjFL37RpvFHjBjRpvMBoM36DLHcAABod8KG//Xuu+/GoYceGtOmTcs7duKJJ8YNN9wQ3bpZdQIAAABr49VzRLzyyiuxxx57NBs0nHrqqXHjjTe6zSUAAAC0UJcPG/7617/GnnvuGbNmzco7dvHFF8e1114bZWVlHd8YAAAAdFJdOmy45ppr4tBDD43Fi5veFqx79+5xww03xAUXXFCkzgAAAKDz6pJ7NtTX18e3vvWtuOaaa/KODRw4MO68887Yf//9i9AZAAAAdH5dLmyora2NcePGNXtry8022yzuu+++2HbbbYvQGQAAAGRDlwobqqqqYuzYsfHYY4/lHdt3333jrrvuiiFDOvm9xwEAAKDIusyeDVVVVXHwwQc3GzSccMIJ8be//U3QAAAAAAl0ibChtrY2xo4dG08//XTesYsuuih+97vfRY8ePYrQGQAAAGRPl1hGccIJJ+TNaOjWrVtcd9118dWvfrVIXQEAAEA2ZT5s+NGPfhR33HFHXv1Xv/pV/Nd//VcROgIAAIBsy/QyipdeeikuuOCCvPq5554raAAAAIB2kumw4Zvf/GYsX768SW3PPfeMSy65pEgdAQAAQPZldhnFQw89FI8++mhe/amnnoqKinSfdmNjY7KxAAAAIAsyO7Ph6quvLnYLAAAA0CVlMmxYtmxZPPjgg8VuAwAAALqkTIYNzz33XNTW1ha7DQAAAOiSMrlnQ2VlZbFboDOoz0XULip2F/lq5resVmy9BkaUZ/JbCAAA0EaZfKXw+c9/3saNrNkLkyIeOCeibnGxO2mZa3cvdgf5eq4TMeaKiJ3GFbsTAACgxGRyGQWsUX2ucwUNpapu8QfPY32u2J0AAAAlRthA11O7SNCQSt3i0lyKAgAAFJWwAQAAAEgqk3s2QKud9kxEnyHF7qL01cwvzf0jSlV7b0La0ZuJ2hQUAIAW8lsjRHwQNPRdt9hdkCXF2oS0PcMgm4ICANBCllEApJbVTUhtCgoAQAsJGwBSy/ImpDYFBQCgBYQNAAAAQFL2bADoCJ11E1KbggIAUABhA0BHsAkpAABdiGUUAAAAQFLCBgAAACApYQMAAACQlLABAAAASMoGkQAAAFBEDfUNUVeTW+Njli1Z0aJac3r2qYhu5R0710DYAAAAAEXy6tS5MeW2mbF82ZrDhubc+oOpLXpcj94Vse+xW8XWo4a1+hqFEjYAAAB0IS15F31VbXlHfVXFeHe91DXUNxQcNLTG8mW5mHLbzNhy1/U77GsgbAAAAOgi2vIu+qpa+o76qorx7nqpq6vJtXvQsNLyZbmoq8lF7/49OuR6wgYAAKAkNOZyUV9VVdC5uYULW1RrjfIBA6KsIjsvmTrqXfTVKca76xRPdv7nAAAAndbiyZNj7iUTo6G6OtmY/zl0bJvO79a/fwy74HuxzuGHJ+qouDryXfTV6eh31zuj8ReNit79urd5nGVLVhQ0AyUVYQMAAFBUjblc8qAhhYbq6ph7ycQYMGZMpmY4UNp69+ueiTDG/xgAAKCo6quqSi5oWKmhujrqq6qiYvDgYrfSLlK9i746xX53neIRNgAAkAltWe/fnPbYA+CjsrYnAJ1PVt5Fp/T4zgYAQKfXHuv9m9PWPQA+Kmt7AqS02f33RfmgQR1+3fqFC+ONxF9n6IqEDQAAdGqlut6/JewJsHrlgwZldukCdAXuNwIAQKdWyuv9W2LlngAAWSJsAAAAAJIyVwsAgMwp1nr/lrAnANAVCBsAAMgc6/0BissyCgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASbn1JQC0t/pcRO2ijr9uzfyW1TpCr4ER5X7tAICuwk99AGhPL0yKeOCciLrFxe7kA9fuXpzr9lwnYswVETuNK871AYAOZRkFALSX+lxpBQ3FVLf4g+eiPlfsTgCADiBsAID2UrtI0LCqusXFWU4CAHQ4YQMAAACQlD0bAKAjnfZMRJ8hxe6iY9TML94eEQBAUQkbAKAj9RkS0XfdYncBANCuLKMAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFIVxW4AgBJRn4uoXdS0VjM//3HN1XoNjCj3IwUAgA/4zRCAiBcmRTxwTkTd4rU/9trd82s914kYc0XETuPS9wYAQKdjGQVAV1efa3nQsDp1iz8Yoz6Xri8AADotMxsAmls+0BYtXXrQFimXLdQualvQsFLd4g/G6rtu28cCAKBTEzZAV9baF9kpXkSX2tr+1iwfaIvmlh60hWULAACUsBL6jR/oUKleZLf2RXQpvUhOsXygWFYuW/jYUe0T3pz2TESfIWt+TM389CEKAACZIGyArqiYL7Lb+0Vya6RaPlAs7blsoc8QyyEAACiYDSKhKyr2i+yVL5IBAIBMMrMBYFUtWT5QLJYtAADQSQgbgA+054vszvQi2fIBAABoM2ED8AEvsgEAgETs2QAAAAAkJWwAAAAAkrKMAgAA6JIac7mor6pqUsstXJj3uOZq5QMGRFmFl1OwOv53AAC0RX2u8Nv51sxvWa0leg2MKPerHbTU4smTY+4lE6Ohunqtj/3PoWPzat36949hF3wv1jn88PZoDzo9P5EAAAr1wqSIB86JqFucbsxC797Tc52IMVdE7DQuXS+QUY25XIuDhtVpqK6OuZdMjAFjxpjhAM2wZwMAQCHqc+mDhraoW/xBP/W5YncCJa++qqpNQcNKDdXVecswgA8IGwAAClG7qHSChpXqFhe+pAMAEjLfBwCgi8s15KJq+Qfvzi5qJqxYtTagx4Co6OZXSLJns/vvi/JBg9b4mPqFC+ONZvZvAPL5SQEAkMppz0T0GdJx16uZX/geD//r3tfvjcumXhbVK1Y/pfyIe4748O/9u/ePc0edG4dtflibrgulpnzQoKgYPLjYbUBmCBsoDW3Zybu1Uu78XQi7hQNkV58hEX3XLXYXLZZryK01aPio6hXVcdnUy+KQTQ8xwwFKUEN9Q9TVNL93y7IlK1pUi4jo2aciupVbdU/h/ISg+NpjJ+/WauO7Qq1it3AASkTV8qpWBQ0rVa+ojqrlVTG4l3eBoZS8OnVuTLltZixf1vKNYm/9wdRm6z16V8S+x24VW48alqo9uhhRFcVVajt5dwS7hQMAkFhDfUOrg4Y1Wb4sF1NumxkN9Q1JxqPrMbOB4irFnbw7wsrdwjvRVFsAuoZ7jrgnBvYa2KS2qHZRk30bgNJTV5NLFjSstHxZLupqctG7f4+k49I1CBsAAPjQwF4DLY8AoM2EDZSejt7JuyMk2C0cAABaY/xFo6J3v+4tfvyyJStWu4cDtJawgdLTyXbyhuRWd3eW1t5JxZ1PAKBL692vuyUQFI3fQgFKSWvvzrKmGTPufAIAQJEIGwBKReq7s6y888nHjmp+hsPKGRRrmzFhhgQAQMEa6huirqb5zTuXLVnRotpKPftURLfyznFTSb89ApSK9rg7y+rufLK2GRSrzpgwQwIAoCCvTp3b6luSrmnfjB69K2LfY7eKrUcNS9Feu+ockQgA6bR2BsXKGRL1aW+nBQCQZQ31Da0OGtZm+bJcTLltZjTUNyQbs72Y2QBQylp7d5aW3PmkkBkUq5shAQBAs+pqckmDhpWWL8tFXU2u5Df/FDYAlDJ3ZwEAoBMSNgCQP4OiJTMkAFijxlwu6quq8uq5hQtbVFupfMCAKKvwazuUotZu/vi5CTtHz77dm318z97lzW7+uGzJijXu41CqfNcCwAwKgMQWT54ccy+ZGA3V1S16/H8OHbvaY936949hF3wv1jn88FTtAQkUsvnjXVc+t9pjnWnzx5awQSQAACTUmMu1KmhYm4bq6ph7ycRozNmoF0pFV9/8sSXMbAAAgITqq6qSBQ0rNVRXR31VVVQMHpx0XEipuSUFzS0laK7Ws09Fs0sISlVX3/yxJYQNAAAAtElrlhQ0t/9A1pYQIGwAAIB2t9n990X5oEEtfnz9woXxxhr2caD0rbpB6Jo2Bc3CBqAplhSsXEKw5a7rd6oZDqsaf9Go6N2v+c0fV6ezbv7YEp37XzUA0PHqcxG1i9b+uJr5Las1p9fAiHK/ppAd5YMGWQLRhbRkg9CVm4JmYQPQVEsKOvsSgt79unfa3tuDn+JAWs29CGnpCw4vLqD0vTAp4oFzIuoWF3Z+S2+p2nOdiDFXROw0rrDrABRJazcIXbkB6IAxYzr9DAdYlX/NQDqteRHS3AsOLy6gtNXn2hY0tEbd4g+u9bGjhJBAp1LIBqFZ3AC0JUsKsryEAGEDkEqKFyFeXEBpq13UMUHDSnWLP7hm33U77poAJGFJAX6bB9JI9SLEiwsAIGM+ukGoDUDpCoQNFK6lG4StSVs2D1sb6/8pZfa2ICtOeyaiz5A0Y9XMb/meDgCdiA1C6Yq65G+rb731Vvz+97+Pxx9/PKZPnx7z58+P2traGDBgQGy++eax++67x5FHHhkHHHBAlJWVFbvd0tTWDcLWJNUvmtb/F19LXoR0xRcX9rYgS/oMMRMJAMjTpcKG9957L84888yYNGlS1NfX5x1fsGBBLFiwIJ599tm49tprY/vtt4+f/vSnceCBBxah2xLWkRuEtYX1/8XnRUg+e1t0DilmbkWYvQUAdFld5reUJ554Io488sh4//33W3zO9OnT46CDDooJEybED3/4w+jWrVs7dtiJdPQGYW1h/b/p+qXG3halrz1nbkWYvQUABWiob4i6mlwsW7Ii79iqtZ59KqJbuddtpaBLvIp47LHH4pBDDomlS5cWdP6VV14ZVVVVcf311yfuDNqZ6frQOp1l5laEGS4AdBmvTp0bU26bGcuX5Zo9vurtM3v0roh9j90qth41rKPaYzUy/9tJZWVlHH300XlBQ7du3eLzn/98HHvssbHVVltFz5494z//+U/cddddccstt8SSJUuaPP5Xv/pVfPzjH4+vf/3rHdl+55Fyg7C26Irr/1fHdP3Ow94WpaMzzdyKMMMFgMxrqG9YY9DwUcuX5WLKbTNjy13XN8OhyDL/6uFrX/tavPvuu01qQ4cOjUmTJsUnP/nJJvUtttgiDjrooPj2t78d48aNi2effbbJ8QkTJsTYsWNj5MiR7d53p2NtfukxXb/z8P8HAKBZdTW5FgcNKy1flou6mlz07t+jnbqiJTIdNjzyyCMxefLkJrW+ffvGAw88EDvvvPNqz9t0003j4Ycfjn322SdeeOGFD+s1NTVxwQUXxI033theLQNQakpl5laEGS4AQKeR6bDh8ssvz6tNnDhxjUHDSv3794977703tttuuyZLKn7/+9/HJZdcYnYDnZPp+tB6Zp4AQEkZf9Go6N2v+4cfL1uyosm+DZSGzC5imTVrVvztb39rUlt//fVbtefCyJEj41vf+laTWi6Xi9/+9rcpWoSOt/JF05r+lMo7uEDHqs9FLH1/zX9WdyebNZ1T37qprwCwNr37dY/e/Xv8359VggdKR2ZnNtxxxx3R0NDQpHb88cdHz549WzXOKaecEhMnTmxSu/XWW+PCCy9sc48AUBLacrvPtc2EclcbAOiSMjuz4cEHH8yrHX744a0eZ6ONNoodd9yxSW3GjBnxxhtvFNwbAJSM9r7d58q72pjhAABdSibDhoaGhnj88ceb1CoqKmLUqFEFjbf33nvn1R577LGCxgKAktIRt/tceVcbAKDLyGTY8O9//zuWLVvWpLbNNttE7969CxrvE5/4RF7tn//8Z0FjAQAAQNZlcs+G6dOn59W22Wabgsfbcsst82qvvvpqweMBQElr6+0+3dUGALq8TIYNs2bNyqttvPHGBY83YsSIFl0DADLB7T4BgDbK5DKKd999N6+24YYbFjzesGHD8mqVlZUFjwcAAABZlsmZDc2FDYMGDSp4vH79+kVFRUXkcv+3k/aSJUtixYoV0b17B93Tden7TT9u7l7nzU15/ejjli3Mf0zvQfnHeq/h+WpujOb6WV29uT5b8g7aR5+DNV13bY8p5Hpr+lzW1EefIau/N/2aPm7r87S2a6Z617JY11vddSKaPnct+TeyprFa8m+4pZ/b0vc77nkqVaX0vay5663u69NcfU09NDf26q5R6P/1llrbc97S7+md+d95e/27W7Yo/5z5/25ZT6l+7qX++nWg3IIF+bWFTT+filV+f/vosbWO/5HHN3d+xUd+P6wYPLhlY3+k94+OXb84f9PVZq/fwuul6Cni/z7f1T2XLe07omW9t+Zr3Nx1Vq2t6d9GIT2t7XopvzapLKte3vTjJSuafFy7tOnHzT0mIqJ3/x7Je2ruOitrKa+Xykefy4jVPFf9uq/2WEuf74iOfw5a8nWJaP++Mhk2VFVV5dUGDBjQpjH79esXixYtyrvOkCEtX9M6Z86cNR6fPXv2h3/Pmznx421bfJ2iuGzXtp1/1itrf0yhz0FzvbXn9VqqkOesrX2ves3VjVWzIKKqoWnt7Xci+tSW/vVSaK+vS8Tqe0/5PLXkcR35mI8q9e9lzWnr97dCpPr/ElHYc17o9801Xa+j/523pKf28NODWva4Evm5t7B2YaxY0PSX0bfnvB01vWpa/biWjrXSa5/cb3WfQRJzP31wq8/Z8tFHWvS4Qnpvrp81XS+3cGHMXdH0+ez99ttRUdOxz+fqnseWPFdt7am1X8O29rTq9VY3Vku+Lqke81E3fveJ1R5bnevOuS+vdtLl+XfaW2nZkuWxcMm8JrU5b8+J3v2af1G6pp5WXrut10v1mJb23RbNPd8RHfMcrKolX5dV+1r1teeqb7C3VVljY2NjstFKxNixY+P+++9vUrvvvvvi0EMPLXjMYcOG5c2YePvtt1u1PKOsrKzg6wMAAEB7euaZZ2K33XZLMlYm92yoq6vLq1VUtG0SR3Pnr1jR/DQZAAAA6MoyuYyivr4+r1ZeXt6mMZs7v6GhoZlHrt6qyySaU1tbGzNmzIihQ4fGeuut1+aABAAAANYkl8vFvHkfLNPYYYcdko2byVezzW3a2Na1J83NYujZs2erxmjuFpoftcUWW7RqTAAAAGiLTTbZJPmYmVxG0aNH/kYZbV3y0FxY0dx1AAAAoKvLZNjQr1+/vNrSpUvbNOaSJUvyar169WrTmAAAAJBFmQwbmrsdZXV1dcHj1dfXx7Jly5rUevXq1WyoAQAAAF1dJsOG9dZbL6/23nvvFTxec+cOHTq04PEAAAAgyzIZNjS3ucXcuXMLHq+5czfYYIOCxwMAAIAsy2TYsPnmm+fVXn/99YLHa+7cbbbZpuDxAAAAIMsyGTbssMMOUVZW1qT2yiuvFDxec+duv/32BY8HAAAAWZbJsGGdddaJLbfcsklt1qxZMW/evILGmzp1al5t1113LWgsAAAAyLpMhg0REZ/61Kfyag8//HCrx1m+fHlMmTKlSa1Pnz6x1157FdwbAAAAZFlmw4YxY8bk1SZNmtTqce6///6822YecMAB0aNHj4J7AwAAgCwra2xsbCx2E+1h+fLlseGGG8b8+fM/rJWXl8dLL70U2267bYvGaGxsjL333jueeuqpJvW77747jjzyyJTtAgAAQGZkdmZDjx494ktf+lKTWn19fXzpS1+K2traFo1x1VVX5QUNG220UYwdOzZZnwAAAJA1mQ0bIiLOOeec6Nu3b5Pa1KlT47jjjltr4HDrrbfGd77znbz6JZdcEhUVFUn7BAAAgCzJdNiw/vrrx8UXX5xXv/vuu2PUqFHx2GOP5R2rrKyMb3zjG3HcccdFQ0NDk2P77LNPnHDCCe3WLwAAAGRBZvdsWKmxsTHGjh0bDzzwQLPHt9xyy/jYxz4W/fr1i1mzZsXTTz8dK1asyHvc+uuvH88991wMHz68vVsGAACATi3zYUNExJIlS+Kwww6LRx55pKDz119//XjooYdihx12SNsYAAAAZFCml1Gs1K9fv/jLX/4Sp59+epSVlbXq3H322SeefvppQQMAAAC0UJeY2bCqF198Ma644oq48847Y9myZc0+pqysLPbYY4/45je/GePGjYtu3bpEJgMAAABJdLmwYaW6urp49tlnY8aMGfH+++9HQ0NDDBo0KEaOHBl77rlnDBkypNgtAgAAQKfUZcMGAAAAoH1YHwAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICkhA0AAABAUsIGAAAAIClhAwAAAJCUsAEAAABIStgAAAAAJCVsAAAAAJISNgAAAABJCRsAAACApIQNAAAAQFLCBgAAACApYQMAAACQlLABAAAASErYAAAAACQlbAAAAACSEjYAAAAASQkbAAAAgKSEDQAAAEBSwgYAAAAgKWEDAAAAkJSwAQAAAEhK2AAAAAAkJWwAAAAAkhI2AAAAAEkJGwAAAICk/j9RGzZb7Zj9UAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 6), dpi=DEFAULT_DPI)\n",
    "dn = dendrogram(\n",
    "    Z,\n",
    "    truncate_mode=\"level\",\n",
    "    show_leaf_counts=False,\n",
    "    no_labels=True,\n",
    "    count_sort=\"descending\",\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find structure with the highest variance per cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"cluster\": y, \"force_std\": metric_values}\n",
    "\n",
    "cluster_df = pd.DataFrame(data).reset_index()\n",
    "\n",
    "max_std_df = (\n",
    "    cluster_df.sort_values([\"cluster\", \"force_std\"], ascending=[True, False])\n",
    "    .groupby(\"cluster\", as_index=False)\n",
    "    .first()\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA plot with the highest variance structure per cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(6, 6), sharex=True, dpi=DEFAULT_DPI)\n",
    "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)\n",
    "\n",
    "for cluster in clusters:\n",
    "    axes[1].scatter(\n",
    "        X_r[y == cluster, 0],\n",
    "        X_r[y == cluster, 1],\n",
    "        edgecolors=None,\n",
    "        linewidths=0,\n",
    "        color=mpl.cm.viridis(cluster / max_index),\n",
    "    )\n",
    "    axes[0].scatter(\n",
    "        X_r[y == cluster, 0],\n",
    "        X_r[y == cluster, 2],\n",
    "        edgecolors=None,\n",
    "        linewidths=0,\n",
    "        color=mpl.cm.viridis(cluster / max_index),\n",
    "    )\n",
    "\n",
    "# highlight max variance per cluster\n",
    "for idx in max_std_df[\"index\"]:\n",
    "    axes[1].scatter(X_r[idx, 0], X_r[idx, 1], linewidths=2, color=\"r\", marker=\"x\")\n",
    "    axes[0].scatter(X_r[idx, 0], X_r[idx, 2], linewidths=2, color=\"r\", marker=\"x\")\n",
    "\n",
    "for ax in axes:\n",
    "    # increase tick size and make them point inwards\n",
    "    ax.tick_params(\n",
    "        axis=\"y\",\n",
    "        direction=\"in\",\n",
    "    )\n",
    "    ax.tick_params(\n",
    "        axis=\"x\",\n",
    "        direction=\"in\",\n",
    "    )\n",
    "\n",
    "# set axes for first plot\n",
    "axes[0].set_ylabel(\"PC 3\")\n",
    "axes[0].set_xticklabels([])\n",
    "axes[0].set_yticklabels([])\n",
    "axes[0].set_xlim([-2, 3])\n",
    "axes[0].set_ylim([-3, 4])\n",
    "\n",
    "# set axes for second plot\n",
    "axes[1].set_xlabel(\"PC 1\")\n",
    "axes[1].set_ylabel(\"PC 2\")\n",
    "axes[1].set_yticklabels([])\n",
    "axes[1].set_ylim([-3, 4])\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=min(clusters), vmax=max(clusters))\n",
    "cb = fig.colorbar(\n",
    "    plt.cm.ScalarMappable(norm=norm, cmap=\"viridis\"),\n",
    "    ax=axes.ravel().tolist(),\n",
    "    shrink=1.00,\n",
    ")\n",
    "cb.set_label(r\"Cluster #\")\n",
    "\n",
    "cb.ax.tick_params(\n",
    "    axis=\"both\",\n",
    "    which=\"major\",\n",
    ")\n",
    "cb.ax.tick_params(\n",
    "    axis=\"both\",\n",
    "    which=\"minor\",\n",
    ")\n",
    "\n",
    "scatter_leg = (\n",
    "    Line2D(\n",
    "        [0],\n",
    "        [0],\n",
    "        marker=\"x\",\n",
    "        lw=0,\n",
    "        markeredgewidth=2,\n",
    "        markeredgecolor=\"r\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "labels = [\"Highest variance in cluster\"]\n",
    "handles = scatter_leg\n",
    "\n",
    "for ax in axes:\n",
    "    ax.legend(handles, labels, loc=\"best\", frameon=True)\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('htvs': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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